Our long-term goal is to understand how humans perform natural tasks given realistic visual input. Object perception is critical for the everyday tasks of recognition, planning, and motor actions. Through vision, we infer intrinsic properties of objects, including their shapes, sizes, materials, as well as their identities. We also infer their depths and movement relationships to each other and ourselves, as well as determine how to use this information. The remarkable fact is that the human visual system provides a high level of functionality despite complex and objectively ambiguous retinal input. Current machine vision systems do not come close to normal human visual competence. In contrast, our daily visual judgments are unambiguous, and our actions are reliable. How is this accomplished? Our conceptual approach to this question is motivated by our previous work on object perception as Bayesian statistical inference, and its implications for how human perception gathers and integrates information about scenes and objects to reduce uncertainty, resolve ambiguity and achieve action goals. Our experimental approach to this question grows out of our team's past accomplishments in using behavioral techniques such as interocular suppression, high-field functional magnetic resonance imaging and analysis, and Bayesian observer analysis of human behavioral performance. We combine our conceptual and experimental approaches to address a new set of questions. In three series of experiments, we aim to better understand: 1) the relationship between cortical activity and the perceptual organization of image features into unambiguous object properties and structures (Within-object interactions);2) how visual information about other objects and surfaces reduces uncertainty about the representation of an object's properties and depth relations (Between-object interactions);and 3) whether and how information and uncertainty may be processed differently depending on the viewer-object interactions demanded by task, as predicted by theory (Viewer-object interactions).